Rethinking Work Design

The previous unit gave you a disciplined method for surfacing the right problems, filtering ruthlessly, and letting early wins generate momentum. But once those pilots start delivering results — and AI begins absorbing routine work — a harder question emerges: what happens to the people, the roles, and the structures around them? The conversation turned directly to this challenge, and the answer wasn't a wholesale org chart overhaul. It was something more practical and more courageous — rethinking how work gets organized around outcomes and employee journeys, mapping people to their adjacent futures, and defending a vision of redeployment over reduction.

You'll recall the conversation acknowledged a tension every people leader feels: rigid hierarchy, job descriptions, and career paths are increasingly mismatched to AI-native work, but "responsibly we can't talk about the complete removal of hierarchy and work charts in all of our companies." The practical move isn't to wait for permission to blow up the org chart — it's to start small. The discussion described pivoting teams toward an outcome and product mindset, putting the employee at the center rather than the function. The concrete example was onboarding: instead of scattering ownership across HR, IT, and the hiring manager's team, you take "the technology, the experience I want to drive, the data and analytics I want to know about that person, the process" and assemble what was called an "outcome squad." The key word is iterate — you build one cross-functional squad around one employee journey, learn from it, and expand. As noted in the talk, "the org chart and hierarchy will eventually [...] deteriorate" — but you don't have to wait for that to start proving the model works. You do need "the courage and the freedom to try those things."

Building on that outcome-based approach, the conversation moved to what might be the single most important responsibility a people leader carries right now: helping employees see themselves in the future. The guidance was specific — use data and assessment to figure out "what are the jobs? What will tech do? Match that up. What's the adjacent job?" and then "map your workforce into that future" through "more personalized journeys around our learning agenda." This isn't classroom training. It's giving people access to experiences — outcome squads, peer development, practice — that let them build skills in the flow of changing work.

And when those skills and AI-driven efficiencies free up capacity, the session offered a powerful proof point. The people operations team at ServiceNow went from a 1-to-450 support ratio to 1-to-1,000 — and critically, the leader noted, "I didn't remove the people from that organization." Instead, freed-up team members moved into higher-difficulty cases, trained the AI to deliver better answers, or built entirely new use cases. The conversation framed this as a "human capital renaissance" — the idea that unleashing people into higher-value work is the real return on AI, not the headcount line on a spreadsheet. When leaders push for capacity reductions, the challenge remains the same one surfaced in the first unit: "What are you going to do with that capacity? What's the end game?" The answer should be growth, not just cuts.